Comparing Static and Dynamic Weighted Software Coupling Metrics | SpringerLink
Skip to main content
Springer Nature Link
Log in

Comparing Static and Dynamic Weighted Software Coupling Metrics

  • Conference paper
  • First Online:
Information and Software Technologies (ICIST 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1078))

Included in the following conference series:

Abstract

Coupling metrics are an established way to measure software architecture quality with respect to modularity. Static coupling metrics are obtained from the source or compiled code of a program, while dynamic metrics use runtime data gathered e.g., by monitoring a system in production. We study weighted dynamic coupling that takes into account how often a connection is executed during a system’s run. We investigate the correlation between dynamic weighted metrics and their static counterparts. We use data collected from four different experiments, each monitoring production use of a commercial software system over a period of four weeks. We observe an unexpected level of correlation between the static and the weighted dynamic case as well as revealing differences between class- and package-level analyses.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    See [19] for a discussion of the relationship between this metric and Spearman’s correlation.

  2. 2.

    A replication package inlcuding the collected data of our experiments will soon be published on Zenodo, to allow other researchers to repeat and extend our work.

References

  1. Ahmad, M.O., Markkula, J., Oivo, M.: Kanban in software development: a systematic literature review. In: 2013 39th Euromicro Conference on Software Engineering and Advanced Applications, pp. 9–16, September 2013. https://doi.org/10.1109/SEAA.2013.28

  2. Allier, S., Vaucher, S., Dufour, B., Sahraoui, H.A.: Deriving coupling metrics from call graphs. In: Tenth IEEE International Working Conference on Source Code Analysis and Manipulation, SCAM 2010, Timisoara, Romania, pp. 43–52, 12–13 September 2010. IEEE Computer Society (2010). https://doi.org/10.1109/SCAM.2010.25

  3. Chug, A., Sharma, H.: Dynamic metrics are superior than static metrics in maintainability prediction: an empirical case study. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO)(Trends and Future Directions), pp. 1–6. IEEE (2015)

    Google Scholar 

  4. Apache Software Foundation: Commons BCEL: Byte code engineering library (2016). https://commons.apache.org/proper/commons-bcel/

  5. Arisholm, E., Briand, L.C., Føyen, A.: Dynamic coupling measurement for object-oriented software. IEEE Trans. Software Eng. 30(8), 491–506 (2004). https://doi.org/10.1109/TSE.2004.41

    Article  Google Scholar 

  6. Atlassian: JIRA project and issue tracking (2017). https://www.atlassian.com/software/jira/

  7. Bogner, J., Wagner, S., Zimmermann, A.: Automatically measuring the maintainability of service-and microservice-based systems: a literature review. In: Proceedings of the 27th International Workshop on Software Measurement and 12th International Conference on Software Process and Product Measurement, pp. 107–115. ACM (2017)

    Google Scholar 

  8. Briand, L., Emam, K.E., Morasca, S.: On the application of measurement theory in software engineering. Empirical Softw. Eng. 1(1), 61–88 (1996). https://doi.org/10.1007/BF00125812

    Article  Google Scholar 

  9. Briand, L.C., Wüst, J.: Empirical studies of quality models in object-oriented systems. Adv. Comput. 56, 97–166 (2002). https://doi.org/10.1016/S0065-2458(02)80005-5

    Article  Google Scholar 

  10. Briand, L.C., Wüst, J., Daly, J.W., Porter, D.V.: Exploring the relationships between design measures and software quality in object-oriented systems. J. Syst. Softw. 51(3), 245–273 (2000). https://doi.org/10.1016/S0164-1212(99)00102-8

    Article  Google Scholar 

  11. Candela, I., Bavota, G., Russo, B., Oliveto, R.: Using cohesion and coupling for software remodularization: is it enough? ACM Trans. Softw. Eng. Methodol. 25(3), 24:1–24:28 (2016). https://doi.org/10.1145/2928268

    Article  Google Scholar 

  12. Carver, R.H., Counsell, S., Nithi, R.V.: An evaluation of the MOOD set of object-oriented software metrics. IEEE Trans. Software Eng. 24(6), 491–496 (1998). https://doi.org/10.1109/32.689404

    Article  Google Scholar 

  13. Chhabra, J.K., Gupta, V.: A survey of dynamic software metrics. J. Comput. Sci. Technol. 25(5), 1016–1029 (2010). https://doi.org/10.1007/s11390-010-9384-3

    Article  Google Scholar 

  14. Chidamber, S.R., Kemerer, C.F.: Towards a metrics suite for object oriented design. In: OOPSLA, pp. 197–211. ACM (1991)

    Google Scholar 

  15. Chidamber, S.R., Kemerer, C.F.: A metrics suite for object oriented design. IEEE Trans. Software Eng. 20(6), 476–493 (1994). https://doi.org/10.1109/32.295895

    Article  Google Scholar 

  16. Cornelissen, B., Zaidman, A., van Deursen, A., Moonen, L., Koschke, R.: A systematic survey of program comprehension through dynamic analysis. IEEE Trans. Software Eng. 35(5), 684–702 (2009)

    Article  Google Scholar 

  17. Fregnan, E., Baum, T., Palomba, F., Bacchelli, A.: A survey on software coupling relations and tools. Inf. Softw. Technol. 107, 159–178 (2019). https://doi.org/10.1016/j.infsof.2018.11.008

    Article  Google Scholar 

  18. Geetika, R., Singh, P.: Dynamic coupling metrics for object oriented software systems: a survey. SIGSOFT Softw. Eng. Notes 39(2), 1–8 (2014). https://doi.org/10.1145/2579281.2579296

    Article  Google Scholar 

  19. Gilpin, A.R.: Table for conversion of Kendall’s tau to Spearman’s rho within the context of measures of magnitude of effect for meta-analysis. Educ. Psychol. Measur. 53, 87–92 (1993). https://doi.org/10.1177/0013164493053001007

    Article  Google Scholar 

  20. van Hoorn, A., Waller, J., Hasselbring, W.: Kieker: a framework for application performance monitoring and dynamic software analysis. In: Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering (ICPE 2012), pp. 247–248. ACM, April 2012. https://doi.org/10.1145/2188286.2188326

  21. Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1/2), 81–93 (1938)

    Article  Google Scholar 

  22. Kiczales, G., Hilsdale, E., Hugunin, J., Kersten, M., Palm, J., Griswold, W.G.: An overview of aspectJ. In: Knudsen, J.L. (ed.) ECOOP 2001. LNCS, vol. 2072, pp. 327–354. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45337-7_18

    Chapter  Google Scholar 

  23. Kirbas, S., et al.: The relationship between evolutionary coupling and defects in large industrial software. J. Softw. Evol. Process 29(4), e1842-n/a (2017). https://doi.org/10.1002/smr.1842,e1842smr.1842

    Article  Google Scholar 

  24. Knoche, H., Hasselbring, W.: Drivers and barriers for microservice adoption - a survey among professionals in Germany. Enterpr. Model. Inf. Syst. Architectures (EMISAJ-Int. J. Conceptual Model.) 14(1), 1–35 (2019). https://doi.org/10.18417/emisa.14.1

    Article  Google Scholar 

  25. Misra, S., Akman, I., Palacios, R.C.: Framework for evaluation and validation of software complexity measures. IET Software 6(4), 323–334 (2012). https://doi.org/10.1049/iet-sen.2011.0206

    Article  Google Scholar 

  26. Mitchell, B.S., Mancoridis, S.: Comparing the decompositions produced by software clustering algorithms using similarity measurements. In: 2001 International Conference on Software Maintenance, ICSM 2001, Florence, Italy, pp. 744–753, 6–10 November 2001. IEEE Computer Society (2001). https://doi.org/10.1109/ICSM.2001.972795

  27. Nagappan, N., Ball, T., Zeller, A.: Mining metrics to predict component failures. In: Proceedings of the 28th international conference on Software engineering (ICSE 2006), pp. 452–461. ACM (2006). https://doi.org/10.1145/1134285.1134349

  28. Offutt, J., Abdurazik, A., Schach, S.R.: Quantitatively measuring object-oriented couplings. Software Qual. J. 16(4), 489–512 (2008). https://doi.org/10.1007/s11219-008-9051-x

    Article  Google Scholar 

  29. Parnas, D.L.: On the criteria to be used in decomposing systems into modules. Commun. ACM 15(12), 1053–1058 (1972). https://doi.org/10.1145/361598.361623

    Article  Google Scholar 

  30. Schnoor, H., Hasselbring, W.: Toward measuring software coupling via weighted dynamic metrics. In: Chaudron, M., Crnkovic, I., Chechik, M., Harman, M. (eds.) Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, ICSE 2018, Gothenburg, Sweden, pp. 342–343, May 27 - June 03 2018. ACM (2018). https://doi.org/10.1145/3183440.3195000

  31. Stevens, W., Myers, G., Constantine, L.: Structured design. In: Yourdon, E.N. (ed.) Classics in Software Engineering, pp. 205–232. Yourdon Press, Upper Saddle River (1979)

    Google Scholar 

  32. Voas, J.M., Kuhn, R.: What happened to software metrics? IEEE Comput. 50(5), 88–98 (2017). https://doi.org/10.1109/MC.2017.144

    Article  Google Scholar 

  33. Yacoub, S.M., Ammar, H.H., Robinson, T.: Dynamic metrics for object oriented designs. In: 6th IEEE International Software Metrics Symposium (METRICS 1999), Boca Raton, FL, USA, pp. 50–61, 4–6 November 1999. IEEE Computer Society (1999). https://doi.org/10.1109/METRIC.1999.809725

Download references

Author information

Authors and Affiliations

  1. Software Engineering Group, Kiel University, Kiel, Germany

    Henning Schnoor & Wilhelm Hasselbring

Authors
  1. Henning Schnoor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wilhelm Hasselbring
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henning Schnoor .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology, Kaunas, Lithuania

    Robertas Damaševičius

  2. Kaunas University of Technology, Kaunas, Lithuania

    Giedrė Vasiljevienė

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schnoor, H., Hasselbring, W. (2019). Comparing Static and Dynamic Weighted Software Coupling Metrics. In: Damaševičius, R., Vasiljevienė, G. (eds) Information and Software Technologies. ICIST 2019. Communications in Computer and Information Science, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-030-30275-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30275-7_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30274-0

  • Online ISBN: 978-3-030-30275-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation